Struct Variogram 

pub struct Variogram {
    pub model: VariogramModel,
    pub nugget: f64,
    pub sill: f64,
    pub range: f64,
}

A fully-specified variogram: a VariogramModel plus its nugget, partial sill and range. Evaluate the semivariance with gamma.

Construct via Variogram::new, which validates the parameters (a valid variogram is what keeps the ordinary-kriging system non-singular).

Fields

model: VariogramModel

Structured-component family.

nugget: f64

Nugget effect c₀ ≥ 0 — the discontinuity at the origin.

sill: f64

Structured partial sill c ≥ 0 — the correlated variance.

range: f64

Range a > 0 (practical range for exponential/Gaussian).

Implementations

impl Variogram

pub fn fit( model: VariogramModel, exp: &ExperimentalVariogram, ) -> Result<Variogram>

Fit a model-shaped variogram to exp by pair-count weighted least squares (see the module docs).

Returns the fitted Variogram (nugget, partial sill and range). Errors with AlgoError::EmptyInput on an empty experimental variogram, and propagates Variogram::new’s validation (e.g. a degenerate all-zero fit). For VariogramModel::Nugget the fit is the weighted-mean semivariance with a nominal (unused) range.

impl Variogram

pub fn new( model: VariogramModel, nugget: f64, sill: f64, range: f64, ) -> Result<Variogram>

Build a variogram, validating the parameters.

Requires nugget ≥ 0, sill ≥ 0, range > 0 (the range is still required — and must be finite/positive — for the Nugget model even though it is unused), and that the model produces a positive semivariance at positive lag. Because the Nugget model ignores sill (γ(h>0) = c₀), a Nugget with nugget = 0 is rejected here — it would otherwise pass a naïve nugget + sill > 0 check yet yield an all-zero Γ and a singular kriging system (surfacing at krige-time as a bogus “duplicate points” error). Invalid parameters yield AlgoError::InvalidArgument.

pub fn total_sill(&self) -> f64

Total sill c₀ + c — the plateau the semivariance approaches at large lags (reached exactly at h ≥ a for the spherical model).

pub fn gamma(&self, h: f64) -> f64

Semivariance γ(h) at lag distance h ≥ 0.

γ(0) = 0. For h > 0 the result is c₀ (nugget) plus the structured component of self.model. A negative h is treated as its absolute value (a variogram is a function of the lag magnitude).

Trait Implementations

impl Clone for Variogram

fn clone(&self) -> Variogram

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Copy for Variogram

impl Debug for Variogram

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for Variogram

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl From<&Variogram> for SpatialVariogram

fn from(value: &Variogram) -> Self

Converts to this type from the input type.

impl From<Variogram> for SpatialVariogram

fn from(value: Variogram) -> Self

Converts to this type from the input type.

impl PartialEq for Variogram

fn eq(&self, other: &Variogram) -> bool

Equality operator ==.

fn ne(&self, other: &Rhs) -> bool

Inequality operator !=.

impl Serialize for Variogram

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl StructuralPartialEq for Variogram